Instructions to use OpenLLM-Ro/RoQwen2-VL-2B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenLLM-Ro/RoQwen2-VL-2B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="OpenLLM-Ro/RoQwen2-VL-2B-Instruct")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("OpenLLM-Ro/RoQwen2-VL-2B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("OpenLLM-Ro/RoQwen2-VL-2B-Instruct", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenLLM-Ro/RoQwen2-VL-2B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenLLM-Ro/RoQwen2-VL-2B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenLLM-Ro/RoQwen2-VL-2B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/OpenLLM-Ro/RoQwen2-VL-2B-Instruct

SGLang

How to use OpenLLM-Ro/RoQwen2-VL-2B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OpenLLM-Ro/RoQwen2-VL-2B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenLLM-Ro/RoQwen2-VL-2B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OpenLLM-Ro/RoQwen2-VL-2B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenLLM-Ro/RoQwen2-VL-2B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use OpenLLM-Ro/RoQwen2-VL-2B-Instruct with Docker Model Runner:
```
docker model run hf.co/OpenLLM-Ro/RoQwen2-VL-2B-Instruct
```

mihaimasala commited on Jun 5

Commit

0cf879c

verified ·

1 Parent(s): 21f1159

Unify Getting Started snippet (consistency + decode slice fix)

Browse files

Files changed (1) hide show

README.md +17 -10

README.md CHANGED Viewed

@@ -269,11 +269,13 @@ than Romanian.
 ```python
 import torch
 from PIL import Image
-from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
 model = Qwen2VLForConditionalGeneration.from_pretrained(
-    "OpenLLM-Ro/RoQwen2-VL-2B-Instruct", torch_dtype=torch.bfloat16, device_map="auto"
-)
 processor = AutoProcessor.from_pretrained("OpenLLM-Ro/RoQwen2-VL-2B-Instruct")
 image = Image.open("example.jpg").convert("RGB")
@@ -281,16 +283,21 @@ question = "Descrie imaginea în detaliu."
 messages = [
     {"role": "user", "content": [
-        {"type": "image"},
         {"type": "text", "text": question},
     ]},
 ]
-text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
-generated = outputs[:, inputs.input_ids.shape[1]:]
-print(processor.batch_decode(generated, skip_special_tokens=True)[0])
 ```
 ## Benchmarks

 ```python
 import torch
 from PIL import Image
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
 model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "OpenLLM-Ro/RoQwen2-VL-2B-Instruct",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+).eval()
 processor = AutoProcessor.from_pretrained("OpenLLM-Ro/RoQwen2-VL-2B-Instruct")
 image = Image.open("example.jpg").convert("RGB")
 messages = [
     {"role": "user", "content": [
+        {"type": "image", "image": image},
         {"type": "text", "text": question},
     ]},
 ]
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device, dtype=torch.bfloat16)
+with torch.inference_mode():
+    outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
+print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
 ```
 ## Benchmarks